Update README.md

README.md

@@ -9,10 +9,10 @@ The main program `CIRIdeep.py` can be used to predict differentially spliced cir
 
 **Prediction with CIRIdeep using total RNA-seq data**
 
-CIRIdeep provides probability of given circRNAs being differentially spliced between any of two samples. When predict with CIRIdeep, expression value of 1499 RBPs (listed in `./demo/RBPmax.tsv`) and splicing amount (derived from SAM alignment files) in both samples are needed. We recommend to process raw total RNA-seq raw fastq files with `CIRIquant`, which provides junction ratio of each circRNA and expression value of each gene in a one-stop manual. SAM files generated with BWA is recommended when producing splicing amount values.
+CIRIdeep provides probability of given circRNAs being differentially spliced between any of two samples. When predict with CIRIdeep, expression value of 1499 RBPs (listed in `./demo/RBPmax_totalRNA.tsv`) and splicing amount (derived from SAM alignment files) in both samples are needed. The order of RBP expression of each sample should keep exactly the same with `RBP max value file`. We recommend to process raw total RNA-seq raw fastq files with `CIRIquant`, which provides junction ratio of each circRNA and expression value of each gene in a one-stop manual. SAM files generated with BWA is recommended when producing splicing amount values.
 
 ```
-python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax.tsv -seqFeature ./demo/cisFeature.tsv -splicing_max ./demo/splicingamountmax.tsv -predict_list ./demo/sample.txt -model_path ./model/CIRIdeep.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_total -splicing_dir ./demo/splicingamount
+python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax_totalRNA.tsv -seqFeature ./demo/cisfeature.tsv -splicing_max ./demo/splicingamountmax_max.tsv -predict_list ./demo/predict_list.txt -model_path ./model/CIRIdeep.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_total -splicing_dir ./demo/splicingamount
 ```
 
 Several files are needed for prediction.
@@ -40,9 +40,9 @@ CIRIdeep(A) gives three probabilities indicating the circRNA being unchanged, ha
 As in some cases, like in scRNA-seq or spatial transcriptomics data, only gene expression matrix is provided, splicing amount is not needed in CIRIdeep(A) any more.
 
 ```
-python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax.tsv -seqFeature ./demo/cisFeature.tsv -predict_list ./demo/sample.txt -model_path ./model/CIRIdeepA.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_total --CIRIdeepA
+python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax_polyA.tsv -seqFeature ./demo/cisfeature.tsv -predict_list ./demo/predict_list.txt -model_path ./model/CIRIdeepA.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_polyA --CIRIdeepA
 ```
 `--CIRIdeepA` When predict using CIRIdeepA, this parameter is needed.
 
-Basically, the input files are similar to CIRIdeep, excluding splicing amount related files. **Notably**, the `RBPmax` file is different from that used in CIRIdeep and all the expression values should be derived from poly(A) selected RNA-seq data.
+Basically, the input files are similar to CIRIdeep, excluding splicing amount related files. **Notably**, the `RBP max value file` file is different from that used in CIRIdeep and all the expression values should be derived from poly(A) selected RNA-seq data. Still, when using CIRIdeep(A), the order of RBP expression of each sample should keep exactly the same with `RBP max value file`.